We conduct a pseudo-Cℓ analysis of the tomographic cross-correlation between 1000 deg2 of weak-lensing data from the Kilo-Degree Survey (KiDS-1000) and the thermal Sunyaev–Zeldovich (tSZ) effect measured by Planck and the Atacama Cosmology Telescope (ACT). Using HMX, a halo-model-based approach that consistently models the gas, star, and dark matter components, we are able to derive constraints on both cosmology and baryon feedback for the first time from these data, marginalising over redshift uncertainties, intrinsic alignment of galaxies, and contamination by the cosmic infrared background (CIB). We find our results to be insensitive to the CIB, while intrinsic alignment provides a small but significant contribution to the lensing–tSZ cross-correlation. The cosmological constraints are consistent with those of other low-redshift probes and prefer strong baryon feedback. The inferred amplitude of the lensing–tSZ cross-correlation signal, which scales as σ8(Ωm/0.3)0.2, is low by ∼2 σ compared to the primary cosmic microwave background constraints by Planck. The lensing–tSZ measurements are then combined with pseudo-Cℓ measurements of KiDS-1000 cosmic shear into a novel joint analysis, accounting for the full cross-covariance between the probes, providing tight cosmological constraints by breaking parameter degeneracies inherent to both probes. The joint analysis gives an improvement of 40% on the constraint of S8 = σ8Ωm/0.3 over cosmic shear alone, while providing constraints on baryon feedback consistent with hydrodynamical simulations, demonstrating the potential of such joint analyses with baryonic tracers such as the tSZ effect. We discuss remaining modelling challenges that need to be addressed if these baryonic probes are to be included in future precision-cosmology analyses.

We constrain the redshift dependence of gas pressure bias ⟨byPe⟩ (bias-weighted average electron pressure), which characterises the thermodynamics of intergalactic gas, through a combination of cross-correlations between galaxy positions and the thermal Sunyaev-Zeldovich (tSZ) effect, as well as galaxy positions and the gravitational lensing of the cosmic microwave background (CMB). The galaxy sample is from the fourth data release of the Kilo-Degree Survey (KiDS). The tSZ y map and the CMB lensing map are from the Planck 2015 and 2018 data releases, respectively. The measurements are performed in five redshift bins with z ≲ 1. With these measurements, combining galaxy-tSZ and galaxy-CMB lensing cross-correlations allows us to break the degeneracy between galaxy bias and gas pressure bias, and hence constrain them simultaneously. In all redshift bins, the best-fit values of ⟨byPe⟩ are at a level of ∼0.3 meV cm−3 and increase slightly with redshift. The galaxy bias is consistent with unity in all the redshift bins. Our results are not sensitive to the non-linear details of the cross-correlation, which are smoothed out by the Planck beam. Our measurements are in agreement with previous measurements as well as with theoretical predictions. We also show that our conclusions are not changed when CMB lensing is replaced by galaxy lensing, which shows the consistency of the two lensing signals despite their radically different redshift ranges. This study demonstrates the feasibility of using CMB lensing to calibrate the galaxy distribution such that the galaxy distribution can be used as a mass proxy without relying on the precise knowledge of the matter distribution.

In this work we present a new method for probing the star formation history of the Universe, namely tomographic cross-correlation between the cosmic infrared background (CIB) and galaxy samples. The galaxy samples are from the Kilo-Degree Survey (KiDS), while the CIB maps are made from Planck sky maps at 353, 545, and 857 GHz. We measure the cross-correlation in harmonic space within 100 < ℓ < 2000 with a significance of 43σ. We model the cross-correlation with a halo model, which links CIB anisotropies to star formation rates (SFRs) and galaxy abundance. We assume that the SFR has a lognormal dependence on halo mass and that the galaxy abundance follows the halo occupation distribution (HOD) model. The cross-correlations give a best-fit maximum star formation efficiency of η max = 0.41 +0.09 −0.14 at a halo mass log 10 (M peak /M ⊙ ) = 12.14 ± 0.36. The derived star formation rate density (SFRD) is well constrained up to z ∼ 1.5. The constraining power at high redshift is mainly limited by the KiDS survey depth. We also show that the constraint is robust to uncertainties in the estimated redshift distributions of the galaxy sample. A combination with external SFRD measurements from previous studies gives log 10 (M peak /M ⊙ ) = 12.42 +0.35 −0.19 . This tightens the SFRD constraint up to z = 4, yielding a peak SFRD of 0.09 +0.003 −0.004 M ⊙ year −1 Mpc −3 at z = 1.74 +0.06 −0.02 , corresponding to a lookback time of 10.05 +0.12 −0.03 Gyr. Both constraints are consistent, and the derived SFRD agrees with previous studies and simulations. This validates the use of CIB tomography as an independent probe of the star formation history of the Universe. Additionally, we estimate the galaxy bias, b, of KiDS galaxies from the constrained HOD parameters and obtain an increasing bias from b = 1.1 +0.17 −0.31 at z = 0 to b = 1.96 +0.18 −0.64 at z = 1.5, which highlights the potential of this method as a probe of galaxy abundance. Finally, we provide a forecast for future galaxy surveys and conclude that, due to their considerable depth, future surveys will yield a much tighter constraint on the evolution of the SFRD.

The location of a galaxy cluster's centroid is typically derived from observations of the galactic and/or gas component of the cluster, but these typically deviate from the true centre. This can produce bias when observations are combined to study average cluster properties. Using data from the BAHAMAS cosmological hydrodynamic simulations we study this bias in both two and three dimensions for 2000 clusters over the 10 13 − 10 15 M mass range. We quantify and model the offset distributions between observationally-motivated centres and the 'true' centre of the cluster, which is taken to be the most gravitationally bound particle measured in the simulation. We fit the cumulative distribution function of offsets with an exponential distribution and a Gamma distribution fit well with most of the centroid definitions. The galaxy-based centres can be seen to be divided into a mis-centred group and a well-centred group, with the well-centred group making up about 60% of all the clusters. Gas-based centres are overall less scattered than galaxy-based centres. We also find a cluster-mass dependence of the offset distribution of gas-based centres, with generally larger offsets for smaller mass clusters. We then measure cluster density profiles centred at each choice of the centres and fit them with empirical models. Stacked, mis-centred density profiles fit to the Navarro-Frenk-White dark-matter profile and Komatsu-Seljak gas profile show that recovered shape and size parameters can significantly deviate from the true values. For the galaxy-based centres, this can lead to cluster masses being underestimated by up to 10%. of galaxy cluster science is the construction of cluster catalogues, which involves cluster identification and the measurement of basic features such as size, profile, redshift, morphology and mass. A crucial element of these measurements that is not often discussed is the definition of cluster centres.Deconstruction of the cluster weak-lensing signal is a well developed and often used technique that allows us to study these clusters, most notably to recover their mass. However, in the process of this measurement, several assumptions about the shape and structure of the cluster have to be made. One of these is the choice of selecting a centre for the cluster. Traditionally, the definition for the 'true' centre of a galaxy cluster is taken to be the deepest point of the gravitational potential well, i.e. where a test particle is most bounded to the system. However, calculating this requires knowing the mass distribution for the cluster in at least 2D (and ideally 3D including redshift) in the first place, which is difficult to do using the lensing signal alone. The work-around that has been employed